code: def forward(self, latent: Tensor) -> Tensor:
      times = (
          self.times.to(latent.device)
          .unsqueeze(0)
          .repeat(latent.shape[0], 1)
      )
      renders = []
      for ki in range(times.shape[1]):
          t_tensor = (
              times[list(range(times.shape[0])), ki]
              .unsqueeze(-1)
              .unsqueeze(-1)
              .unsqueeze(-1)
              .repeat(1, 1, latent.shape[2], latent.shape[3])
          )
          latenti = concatenate((t_tensor, latent), 1)
          result = self.net(latenti)
          renders.append(result)
      renders_stacked = stack(renders, 1).contiguous()
      renders_stacked[:, :, :4] = torch.sigmoid(
          renders_stacked[:, :, :4]
      )
      return renders_stacked
  
imports: from typing import Callable, Dict, List, Optional, Type
  import torch
  from torch import nn
  from kornia.core import Module, Tensor, concatenate, stack
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 2 for calling the RenderingDeFMO obj's underlying member method code with example usage: obj.forward(latent) and returns: renders_stacked